Decisions on whether or not to get vaccinated for seasonal in?uenza are largely motivated by attitudes and beliefs of the risks of infection and bene?ts of being vaccinated. The risk of in?uenza infection can change from season to season and depends on one's own vaccination status and the vaccination coverage among one's social net- work. Furthermore, attitudes and beliefs related to risk of infection can spread over a social network. Thus, in addition to personal attitudes, beliefs and experiences with vaccination and treatments for in?uenza, interactions of individuals on and characteristics of the social network can play important roles in shaping the nature and severity of in?uenza outbreaks and the effectiveness and cost of promoting vaccination. Our previous exploratory research has con?rmed a strong dynamical interplay between behavior to get vaccinated, in?uenza epidemiology and social network structures. We collected nationally-representative cross- sectional survey data on behavioral factors associated with the decision to seek in?uenza vaccination. We then used these data to inform the development of an innovative agent-based model (ABM) that allowed experiences from past in?uenza seasons affect decisions to get vaccinated in the current season, and thus in?uence the course of an epidemic at the population level. In contrast to past and standard approaches, our models include two important properties of human decision-making: (a) memory and adaptability from past experiences and (b) peer-in?uences via rumor/information spreading. However, our ABM assumed a demographically homogenous population, considered just idealized social network structures and only considered a reduced set of attitudes and beliefs that affect the behavior to get vaccinated as suggested by our survey. In the proposed research we are interested in enhancing and re?ning our ABM by allowing our population to vary in terms of the demographic characteristics that in?uence vaccination, predisposition towards vaccination, and exposure to advice and opportunities for vaccination. We will conduct a four-year longitudinal panel study to construct an empirical behavioral model of decisions to get vaccinated for seasonal in?uenza that will include questions on additional attitudinal factors and considers a wider set of behavioral mechanisms. We will construct an improved social contact network structure that is representative of a large town/small city within the United States (US). We will consider different overlaying social contact network structures representative of different types of mixing. This approach will allow us to model social interactions and disease spread at a ?ner granularity and a higher level of realism than any existing random network model. We will calibrate our model in order to reproduce general yearly US trends of vaccination rates and infections by socio-demographic strata. We will then use our model to evaluate how behavioral and attitudinal factors in?uence the effectiveness of policies based on alternative vaccination promotion strategies and incentive-based strategies, and the expected changes when universal vaccines become available and awareness of the availability of antivirals increases.